Biomarkers of Rapid Progression in Advanced Non-Small Cell Lung Cancer

ABSTRACT

Methods and kits for identifying rapidly progressing lung cancer in a subject are provided. The method includes obtaining a biological sample from the subject and assaying a level of a biomarker in a biomarker panel in the biological sample where the panel includes at least one biomarker selected from Table I or Table II. The method further includes determining with the subject is treatment naïve or has received at least one treatment; and comparing the level of the biomarker in the subject&#39;s sample to a cutoff value listed in Table I for treatment naïve subjects or Table II for previously treated subjects. The method further includes determining whether the subject&#39;s level is above or below the cutoff value to determine whether the subject has rapidly progressing lung cancer.

RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 15/102,647,filed Dec. 8, 2014, which claims the benefit under 35 U.S.C. § 371 ofInternational Application No. PCT/US2014/069009, filed Dec. 8, 2014,which claims the benefit of U.S. Provisional Application No. 61/913,740,filed Dec. 9, 2013, which is incorporated herein in their entirety.

TECHNICAL FIELD

The present invention relates to methods and kits for identifyingpatients with rapidly progressing disease, and in particular to methodsand kits for identifying patients with rapidly progressing non-smallcell lung cancer and for determining optimal treatment plans forpatients with rapidly progressing disease and for monitoring treatments.

BACKGROUND

Lung cancer is leading cause of cancer-related mortality worldwide, witha projected 159,480 patients succumbing to the disease in the US in2014.(1) Lung cancer is typically characterized as being quiteaggressive with poor clinical outcomes that stem from the very rapidproliferation rates, high metastatic potential, and generalinsensitivity to available treatment strategies. Non-small cell lungcancer (NSCLC) presents unique challenges to health care providersbecause of its common late stage of presentation and the poor medianoverall survival of advanced disease.(2, 3) Patients often become tooill to receive second line treatment as noted by a recent phase IIIclinical trial where only 37% of the patients randomized to docetaxel atdisease progression received treatment.(4)

An objective of this study was reveal circulating biomarkers to identifypatients with rapidly progressing NSCLC. This study examined 76biomarkers that are surrogates for several pathophysiological processesassociated with aggressive disease in both frontline (chemo naïve) andsecond-line and greater (pretreated) patients. A total of 186 patientserum specimens were evaluated. Processes evaluated includeangiogenesis, phenotypic transdifferentiation (i.e. EMT, cancer stemcells), cancer cachexia, chronic inflammation, and immune systemresponse.

Identification of patients with rapidly-progressing disease who areinsensitive to standard platinum double-based chemotherapy will provideclinical implications.

There is a need in the art for screening methods and kits that identifypatients with rapidly progressing disease in patients that are treatmentnaïve and in patients that have received a treatment.

BRIEF SUMMARY

Methods and kits for identifying rapidly progressing lung cancer in asubject are provided. The method includes obtaining a biological samplefrom the subject and assaying a level of a biomarker in a biomarkerpanel in the biological sample where the panel includes at least onebiomarker selected from Table I or Table II. The method is dependent onwhether the subject is treatment naïve or has received at least onetreatment; and comparing the level of the biomarker in the subject'ssample to a cutoff value listed in Table I for treatment naïve subjectsor Table II for previously treated subjects. The method further includesdetermining whether the subject's level is above or below the cutoffvalue to determine whether the subject has rapidly progressing lungcancer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F illustrate representative ‘Box and Whisker’ plots indicatingdistribution of biomarker levels in the frontline chemotherapy cohort,separated based on a 90 day cutoff for rapid disease progression. Shownare TNF-α (panel 1A), sTNFRII (panel 1B), IL-6 (panel 1C), sVEGFR3(panel 1D), betacellulin (panel 1E) and Total PSA (panel 1F). Allbiomarker levels are provided in pg/mL.

FIGS. 2A-2F illustrate representative ‘Box and Whisker’ plots indicatingdistribution of biomarker levels in the treated chemotherapy cohort,separated based on a 45 day cutoff for rapid disease progression. Shownare sEGFR (panel 2A), sTNFRI (panel 2B), TRAIL (panel 2C), IGFBP-1(panel 2D), IGFBP-2 (panel 2E) and HGF (panel 2F). All biomarker levelsare provided in pg/mL.

DETAILED DESCRIPTION

The present invention will utilize at least one biomarker measured in abiological sample obtained from a subject to identify rapidlyprogressing lung cancer, and in some embodiments in subjects havingrapidly progressing NSCLC. In some embodiments, the at least onebiomarker may be selected from a panel of biomarkers. In someembodiments, one or more biomarkers from a panel of biomarkers are usedto identify subjects having rapidly progressing NSCLC in subjects thatare treatment naïve or that have been previously treated.

The term “biomarker” as used herein, refers to any biological compoundthat can be measured as an indicator of the physiological status of abiological system. A biomarker may comprise an amino acid sequence, anucleic acid sequence and fragments thereof. Exemplary biomarkersinclude, but are not limited to cytokines, chemokines, growth andangiogenic factors, metastasis related molecules, cancer antigens,apoptosis related proteins, proteases, adhesion molecules, cellsignaling molecules and hormones.

“Measuring” or “measurement” means assessing the presence, absence,quantity or amount (which can be an effective amount) of a givensubstance within a sample, including the derivation of qualitative orquantitative concentration levels of such substances, or otherwiseevaluating the values or categorization of a subject's clinicalparameters. Alternatively, the term “detecting” or “detection” may beused and is understood to cover all measuring or measurement asdescribed herein.

The terms “sample” or “biological sample” as used herein, refers to asample of biological fluid, tissue, or cells, in a healthy and/orpathological state obtained from a subject. Such samples include, butare not limited to, blood, bronchial lavage fluid, sputum, saliva,urine, amniotic fluid, lymph fluid, tissue or fine needle biopsysamples, peritoneal fluid, cerebrospinal fluid, and includes supernatantfrom cell lysates, lysed cells, cellular extracts, and nuclear extracts.In some embodiments, the whole blood sample is further processed intoserum or plasma samples. In some embodiments, the sample includes bloodspotting tests.

The term “subject” or “patient” as used herein, refers to a mammal,preferably a human.

The term “rapid progression” or “rapidly progressing” as used herein,refers to cases of disease that were observed to not respond tochemotherapy or targeted agents and advance (evidence of nascentmetastases, increasing tumor volume, etc.) within a defined timeinterval. Thresholds for rapid progression were set to 90 days after thefirst treatment for the treatment naïve patients and 45 days after thesecond or subsequent treatment for the previously treated patients.Circulating levels of 27 biomarkers were found to be significantlyassociated (p≤0.05) with progression within 90 days of treatmentinitiation in treatment naive patients. Circulating levels of 34biomarkers were found to be significantly associated (p≤0.05) withprogression within 45 days of treatment initiation in previously treatedpatients.

Biomarkers

Biomarkers that may be used include but are not limited to cytokines,chemokines, growth and angiogenic factors, metastasis related molecules,cancer antigens, apoptosis related proteins, proteases, adhesionmolecules, cell signaling molecules and hormones. In some embodiments,the biomarkers may be proteins that are circulating in the subject thatmay be detected from a fluid sample obtained from the subject. In someembodiments, the fluid sample may be serum or plasma. In someembodiments, one or more biomarkers from a panel of biomarkers may beused.

In some embodiments, one or more biomarkers may be measured in abiomarker panel. The biomarker panel may include a plurality ofbiomarkers. In some embodiments, the biomarker panel may include ten orfewer biomarkers. In yet other embodiments, the biomarker panel mayinclude 1, 2, 3, 4, 5, 6 or 7 biomarkers. In some embodiments, thebiomarker panel may be optimized from a candidate pool of biomarkers. Byway of non-limiting example, the biomarker or biomarker panel may beconfigured for determining whether a treatment naïve subject is likelyto have rapidly progressing disease. In some embodiments, the biomarkeror biomarker panel may be configured for determining whether apreviously treated subject is likely to have rapidly progressingdisease.

In some embodiments, the biomarker panel may include biomarkers fromseveral biological pathways. By way of non-limiting example, thebiomarkers may be associated the tumor necrosis factor (TNF) family, theepidermal growth factor (EGF) family, the vascular endothelial growthfactor (VEGF) family, the Insulin-like growth factor (IGF) family and/orassociated with angiogenesis. In some embodiments, the TNF family mayinclude, but is not limited to TNF-RI, TNF-RII, TNF-α and TRAIL. In someembodiments, the EGF family may include but is not limited tobetacellulin, amphiregulin, and soluble-EGFR. In some embodiments, theVEGF family may include but is not limited to VEGF-A, VEGF-C, andsoluble-VEGFR3. In some embodiments, the IGF family may include but isnot limited to IGF-I, IGF-II, IGFBPs-2, -3, and -7. In some embodiments,the biomarkers associated with angiogenesis may include follistatin,IL-6, endoglin, PDGF-BB, IGF-1, and endothelin-1, PLGF, IL-8, MMP-2,HGF, sVEGFR2, VEGF-A, leptin, PDGF-AA, and others. In some embodiments,the biomarker panel may include one or more biomarkers from a panel ofbiomarkers. In some embodiments, the one or more biomarkers may beselected from the list of biomarkers in Table I. In some embodiments,the one or more biomarkers may be selected from the list of biomarkersin Table II. In some embodiments, other biomarkers may be used and maybe combined with the biomarkers listed in Tables I and II.

In some embodiments, patients with rapid disease progression in atreatment naïve group may be identified using one or more biomarkersselected from a panel of biomarkers listed Table I. In some embodiments,the one or more biomarkers may be selected from the group of biomarkersidentified in Table I as having a p-value of 0.01 or less. In someembodiments, the one or more biomarkers may include at least onebiomarker from Table I having a p-value of 0.01 or less and at least onebiomarker from Table I having a p-value of 0.05 or less. In someembodiments, the one or more biomarkers may include biomarkers selectedfrom the group consisting of sTNFRI, sTNFRII, CA 19-9, Follistatin,Total PSA, TNF-α and IL-6. In some embodiments, the biomarkers mayinclude 3, 4, 5, 6 or 7 biomarkers selected from the group consisting ofsTNFRI, sTNFRII, CA 19-9, Follistatin, Total PSA, TNF-α and IL-6 and mayalso include additional biomarkers. In some embodiments, patients withrapid disease progression in a treatment naïve group may be identifiedusing a panel of one or more biomarkers selected from Table I where thepanel may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 biomarkers.

In some embodiments, patients with rapid disease progression in apreviously treated group may be identified using one or more biomarkersselected from a panel of biomarkers selected from Table II. In someembodiments, the one or more biomarkers may be selected from the groupof biomarkers identified in Table II as having a p-value of 0.01 orless. In some embodiments, the one or more biomarkers may include atleast one biomarker from Table II having a p-value of 0.01 or less andat least one biomarker form Table II having a p-value of 0.05 or less.In some embodiments, the one or more biomarkers may be selected from thegroup consisting of TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF,MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6. In someembodiments, the one or more biomarkers may include 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12 or 13 biomarkers selected from the group consisting ofTRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7, MMP-2,α-fetoprotein, Osteopontin, sVEGFR2 and IL-6 and may also includeadditional biomarkers. In some embodiments, patients with rapid diseaseprogression in a previously treated group may be identified using one ormore biomarkers selected from Table II where the panel may include 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 or 34 biomarkers.

In some embodiments, the biomarker panel may be selected using areference profile that can be made in conjunction with a statisticalalgorithm used with a computer to implement the statistical algorithm tosort the subject into a group. In some embodiments, the statisticalalgorithm is a learning statistical classifier system. The learningstatistical classifier system can be selected from the following list ofnon-limiting examples, including Random Forest (RF), Classification andRegression Tree (CART), boosted tree, neural network (NN), supportvector machine (SVM), general chi-squared automatic interaction detectormodel, interactive tree, multiadaptive regression spline, machinelearning classifier, and combinations thereof. By way of non-limitingexample, exemplary tools for selecting a biomarker panel may be found inWO 2012/054732 and U.S. Provisional Application No. 61/792,710 which areincorporated by reference herein.

Biomarker Measurement

Measurement of a biomarker generally relates to a quantitativemeasurement of an expression product, which is typically a protein orpolypeptide. In some embodiments, the measurement of a biomarker mayrelate to a quantitative or qualitative measurement of nucleic acids,such as DNA or RNA. The measurement of the biomarker of the subjectdetects expression levels of one or more biomarkers in subjects havinglung cancer and in some embodiments, compares the expression level ofeach biomarker measured to a cutoff value listed in Table I or in TableII.

Expression of the biomarkers may be measured using any method known toone skilled in the art. Methods for measuring protein expressioninclude, but are not limited to Western blot, immunoprecipitation,immunohistochemistry, Enzyme-linked immunosorbent assay (ELISA), RadioImmuno Assay (RIA), radioreceptor assay, proteomics methods,mass-spectrometry based detection (SRM or MRM) or quantitativeimmunostaining methods. Methods for measuring nucleic acid expression orlevels may be any techniques known to one skilled in the art. Expressionlevels from the one or more biomarkers are measured in the subject andcompared to the levels of the one or more biomarkers obtained from acohort of subjects described below.

In some embodiments, MILLIPLEX® MAP multiplex assays may be used todetermine the expression levels of the one or more biomarkers in a panelof biomarkers. (EMD Millipore, Billlerica, Mass.) In some embodiments,Luminex-based xMAP® multiplexed immunoassays may be used to determinethe expression levels of the panel of biomarkers. (Luminex Corp.;Austin, Tex.) In some embodiments, biomarker concentrations may becalculated based on 7-point standard curves using a five-parametric fitalgorithm in xPONENT v4.0.3 (Luminex Corp.) Other measurement systemsand techniques may also be used.

In some embodiments, a kit may be provided with reagents to measure atleast one biomarker. In some embodiments, the kit may be provided withreagents to measures at least two biomarkers in a panel of biomarkers.The panel of biomarkers to be measured with the kit may include two ormore biomarkers from Table I or Table II. The kit may include reagentsto measure a panel of biomarkers for subjects that are treatment naïve.The kit may include reagents to measure a panel of biomarkers forsubjects that have been previously treated.

Analysis of Biomarker Measurements

In some embodiments, methods for determining whether a subject hasrapidly progressing lung cancer may be based upon the biomarkermeasurements from the subject compared to a reference cutoff level foreach biomarker measured. The reference cutoff level for a plurality ofbiomarkers is listed in Tables I-IV.

Treatment Stratification

In some embodiments, the analysis of the biomarker panel may be used todetermine a treatment regime for the subject. In some embodiments, themeasurement of one or more biomarkers in the panel may be used todetermine whether to begin a treatment, to continue the same treatmentor to modify the treatment regime for a subject. The treatment may bemodified by changing the drug administered to the subject or to add anadditional drug to the existing drug treatment regime, to change thedosage or other changes. In some embodiments, other types of treatmentregimes may be used such as radiation. In some embodiments, theidentification of patients with rapidly progressing disease who areinsensitive to standard platinum doublet-based chemotherapy may havemultiple clinical implications. The identification of patients withrapidly progressing disease using the biomarker level may place thepatient in a specific treatment, a different treatment or an earliertreatment in the overall treatment strategy. In some embodiments, aspecific targeted chemotherapeutic agent may be selected based on theidentification of rapidly progressing disease. In some embodiments, thespecific chemotherapeutic agent may be changed based in the biomarkerlevel measured relative to the cutoff value. By way of non-limitingexample, VEGF-A levels in patients taking bevacizumab may be monitoredand the treatment regime may be changed or not changed based on thelevel of VEGF-A measured and compared to the cutoff level in eitherTable I for treatment naïve patients or Table II for previously treatedpatients.

Patient Cohorts

Between 2004 and 2011, 186 patients at Rush University Medical Center(Chicago, Ill.) were enrolled and divided into the following cohorts:patients with advanced lung adenocarcinoma naïve to previouschemotherapy (n=76) and patients with advanced lung adenocarcinoma thathave failed at least 1 line of chemotherapy (n=110). All stageclassifications were determined according to the American JointCommittee on Cancer (AJCC) seventh edition criteria and confirmed bypathological evaluation (5, 6). All patient data was obtained afterinformed consent was given by the patient. The study was conducted inabsolute compliance with the Institutional Review Board at RushUniversity Medical Center.

Measurement of Serum Biomarker Concentrations

All peripheral blood was collected pre-treatment and processed intoserum using standard phlebotomy protocols. Serum was archived at −80° C.in aliquots; and no evaluable specimen were subjected to more than twofreeze-thaw cycles (7-10). Serum was evaluated using the followingbiomarker panels: the MILLIPLEX® MAP Human Angiogenesis/Growth FactorPanel (EMD Millipore, Billerica, Mass.) and included the followingassays: epidermal growth factor (EGF), angiopoietin-2, granulocytecolony-stimulating factor (G-CSF), bone morphogenic protein 9 (BMP-9),endoglin, endothelin-1, leptin, fibroblast growth factor-1 (FGF-1),FGF-2, follistatin, interleukin-8 (IL-8), hepatocyte growth factor(HGF), heparin-binding epidermal growth factor (HB-EGF), placentalgrowth factor (PLGF), vascular endothelial growth factor-A (VEGF-A),VEGF-C and VEGF-D; the MILLIPLEX® MAP Human Soluble Cytokine ReceptorPanel which includes sVEGFR1, sVEGFR2, sVEGFR3, sIL-6R, sgp130, sTNFRI,and sTNFRII; the MILLIPLEX® MAP Human Circulating Cancer Biomarker Panel1 which includes sFasL, IL-6, prolactin, SCF, TGF-α, and TNF-α; theMILLIPLEX® MAP Human MMP1 and MMP2 panels which combine to provideMMPs-1, -2, -3, -7, -9, and -10; and the MILLIPLEX® MAP HumanCytokine/Chemokine Panel II, which includes SDF-1 (α+β). All assays wereperformed according to the manufacturer's recommended protocols and in ablinded fashion. All data was collected on a Luminex FlexMAP 3D systemwith concentrations calculated based on 7-point standard curves using afive-parametric fit algorithm in xPONENT v4.0.3 (Luminex Corp., Austin,Tex.).

Statistical Methods

One endpoint of the investigation was to evaluate associations of thecirculating biomarkers tested with clinical outcome measures forpatients determined to have rapidly progressing disease. Progressionstatus values were classified as ‘slow’ or ‘rapid’ based on chosenclinically-relevant cutoff (45 days for frontline and 90 days for thosesecond-line and above) value. Association of the slow/rapid progressionstate was then accomplished with low and high values of a biomarkerbased on cutoff values obtained from a grid search for an optimal cutoffwithin the potential range of the biomarker values that maximizes thep-value for disease progression via Fisher's exact test. Additionally,an adjusted p-value, which adjusts for the grid search, is alsoobtained. These analyses were performed regardless of regimen type. Allstatistical analyses were completed using the R Statistical Package.

Results

Frontline Treatment for Advanced NSCLC

A total of 27 biomarkers were identified for identifying advanced stageNSCLC patients that were chemotherapy naïve with rapidly progressingdisease. The specific cutoff values, number of patients in each arm, andoptimal p-values are all provided in Table I, with the distribution ofthese classifications for select representative biomarkers provided inFIG. 1. A complete account for all 76 biomarkers is provided in TableIII. Included in the findings were biomarkers with optimal p-value ≤0.05representing the following processes: angiogenesis (sTNFRI, sTNFRI I,Follistatin, TNF-α, Betacellulin, sVEGFR3, VEGF-A, Endoglin, MMP-10,PDGF-BB, VEGF-C, IGF-I, IGFBP-3, IGFBP-5, Endothelin-1, andAmphiregulin), cancer cachexia (TNF-α, sTNFRI, sTNFRII, IGF-I, IGFBP-3,IGFBP-5, IL-6, IL-6R), and phenotypic transdifferentiation(betacellulin, IGF-I, IGFBP-3, IGFBP-5, sEGFR, and prolactin).

TABLE I Treatment Naïve Cohort Cutoff Value Cases ≤ Cases > AdjustedBiomarker (pg/mL) cutoff Cutoff p-Value p-value sTNFRI 1879.8 48 120.0003 0.001 sTNFRII 8493.9 49 11 0.0011 0.006 CA 19-9 20.9 62 13 0.00290.018 Follistatin 990.0 48 15 0.0033 0.007 Total PSA 67.1 47 28 0.00420.027 TNF-α 0.61 12 45 0.0050 0.029 IL-6 13.1 48 10 0.0100 0.021 TGF β130665.9 64 11 0.0132 0.083 Betacellulin 14.9 25 28 0.0162 0.058 CA 15.355.0 62 13 0.0203 0.077 sCD30 101.5 40 20 0.0219 0.111 sVEGFR3 2930.8 4812 0.0221 0.094 VEGF-A 727.1 49 27 0.0222 0.092 Endoglin 993.8 48 150.0228 0.084 MMP-10 272.6 43 32 0.0244 0.122 sRAGE 84.2 39 21 0.02440.121 PDGF-BB 13336.6 12 40 0.0248 0.075 VEGF-C 32.9 10 53 0.0255 0.105IGF-I 31266.8 46 30 0.0264 0.133 IGFBP-3 1452.8 53 23 0.0308 0.168 sEGFR25073.2 29 43 0.0392 0.177 IGFBP-5 151.1 48 28 0.0428 0.192 Endothelin-18.0 39 24 0.0430 0.124 Amphiregulin 38.7 16 36 0.0445 0.222 GRO 3960.347 28 0.0453 0.181 sIL-6R 10666.7 20 40 0.0493 0.203 Prolactin 5353.7 3028 0.0497 0.152

Previous Treatment for Advanced NSCLC

A total of 34 biomarkers were identified for identifying advanced stageNSCLC patients that failed frontline chemotherapy with rapidlyprogressing disease. The specific cutoff values, number of patients ineach arm, and optimal p-values are all provided in Table II, with thedistribution of these classifications for select representativebiomarkers provided in FIG. 2. A complete account for all 76 biomarkersare provided in Table IV. Included in the findings were biomarkers withoptimal p-value D105 representing the following processes: angiogenesis(sVEGFR2, follistatin, IGF-I, IGF-II, IGFBPs-1, -2, -3, -5, -7, IL-8,MMP-2, MMP-7, PLGF, Leptin, PDGF-AA, TNF-α, sTNFRI, sTNFRII, andVEGF-A), cancer cachexia (HGF, IGF-I, IGF-II, IGFBPs-1, -2, -3, -5, -7,IL-6, Leptin, TRAIL, sCD30, TNF-α, sTNFRI, and sTNFRII), phenotypictransdifferentiation (beta-HCG, α-fetoprotein, HE4, HGF, IGF-I, IGF-II,IGFBPs-1, -2, -3, -5, -7, osteopontin, PLGF, TGF-β, and sEGFR), andinflammation or immune response (CYFRA 21-1, GRO, IL-6, IL-8, sIL-2Rα,sIL-4R, TRAIL, sCD30, TNF-α, sTNFRI, and sTNFRII).

TABLE II Post Treatment Cohort Cutoff Value Cases ≤ Cases > AdjustedBiomarker (pg/mL) cutoff Cutoff p-Value p-value TRAIL 60.3 35 76 0.00050.003 sTNFRI 1506.6 62 34 0.0007 0.005 IGFBP-1 9.2 88 22 0.0009 0.005sEGFR 50565.2 36 68 0.0012 0.007 IGF-I 7807.7 32 78 0.0021 0.008 TGF-β19073.7 10 77 0.0028 0.016 HGF 239.7 81 30 0.0035 0.034 MMP-7 8326.9 6843 0.0043 0.024 MMP-2 33782.5 31 80 0.0044 0.027 α-fetoprotein 964.1 6340 0.0050 0.031 Osteopontin 34067.2 49 54 0.0058 0.034 sVEGFR2 10846.513 83 0.0073 0.049 IL-6 8.0 66 37 0.0098 0.035 CYFRA21.1 768.7 51 520.0119 0.03 IGF-II 507.7 37 68 0.0129 0.083 sTNFRII 7588.9 65 31 0.01510.093 CA 15.3 34.5 83 28 0.0156 0.056 sCD30 129.7 73 23 0.0177 0.081sIL-4R 2096.7 14 82 0.0201 0.103 sIL-2Ralpha 836.6 55 41 0.0208 0.118IGFBP-7 53.7 18 92 0.0211 0.137 TNF-α 91.1 93 17 0.0213 0.114 VEGF-A1297.7 98 14 0.0227 0.05 Follistatin 968.9 11 10 0.0237 0.013 Leptin-111840.0 55 48 0.0239 0.154 IGFBP-3 1486.8 90 20 0.0263 0.161 PLGF 86.869 10 0.0268 0.127 IGFBP-5 193.5 44 66 0.0296 0.166 GRO 4254.6 80 300.0333 0.162 IL-8 63.9 96 15 0.0334 0.178 HE4 2993.4 88 23 0.0343 0.201IGFBP-2 22.9 64 46 0.0344 0.169 PDGF-AA 10851.4 12 98 0.0375 0.218 B-HCG0.16 26 85 0.0446 0.239

TABLE III Treatment Naïve Cohort Prop. ≤ No. ≤ Prop. > No. > TotalOptimal Adjusted Biomarker Cutoff cutoff Cutoff cutoff Cutoff No.p-value p-value sTNFRI 1879.789 0.75 48 0.166667 12 60 0.000344 0.001sTNFRII 8493.872 0.734694 49 0.181818 11 60 0.001094 0.006 CA.19.920.91498 0.758065 62 0.307692 13 75 0.00293 0.018 Follistatin 990.00750.625 48 1 15 63 0.003303 0.007 Total.PSA 67.1 0.808511 47 0.464286 2875 0.004239 0.027 TNF.alpha 0.614 1 12 0.577778 45 57 0.005026 0.029IL.6 13.09553 0.75 48 0.3 10 58 0.009969 0.021 TGF.beta1 30665.91 0.62564 1 11 75 0.013211 0.083 Betacellulin 14.915 0.88 25 0.571429 28 530.016219 0.058 CA.15.3 55.03582 0.741935 62 0.384615 13 75 0.0203380.077 sCD30 101.452 0.525 40 0.85 20 60 0.021891 0.111 sVEGFR3 2930.8410.708333 48 0.333333 12 60 0.022146 0.094 VEGF 727.1049 0.591837 490.851852 27 76 0.02221 0.092 Endoglin 993.8066 0.791667 48 0.466667 1563 0.022804 0.084 MMP.10 272.6212 0.790698 43 0.53125 32 75 0.0244060.122 SRAGE 84.19616 0.74359 39 0.428571 21 60 0.024413 0.121 PDGF.BB13336.56 0.416667 12 0.8 40 52 0.024825 0.075 VEGF.C 32.93148 0.4 100.773585 53 63 0.025532 0.105 IGF.1 31266.81 0.586957 46 0.833333 30 760.026375 0.133 IGFBP.3 1452.848 0.603774 53 0.869565 23 76 0.0308290.168 sEGFR 25073.18 0.827586 29 0.581395 43 72 0.039227 0.177 IGFBP.5151.1263 0.770833 48 0.535714 28 76 0.042771 0.192 Endothelin.1 8.0120.615385 39 0.875 24 63 0.043036 0.124 Amphiregulin 38.666 0.5 160.805556 36 52 0.044476 0.222 GRO 3960.296 0.765957 47 0.535714 28 750.045349 0.181 sIL.6R 10666.72 0.45 20 0.725 40 60 0.049324 0.203Prolactin 5353.744 0.8 30 0.535714 28 58 0.049721 0.152 sIL.2Ralpha1390.342 0.702128 47 0.384615 13 60 0.051904 0.177 FGF.2.1 77.315470.744186 43 0.466667 15 58 0.061694 0.192 VEGF.D 102.6913 0.782609 460.529412 17 63 0.063445 0.242 PDGF.AA 47558.59 0.607843 51 0.833333 2475 0.065221 0.315 Tenascin.C 1130.558 0.84 25 0.592593 27 52 0.0684690.207 RANTES 38837 0.821429 28 0.595745 47 75 0.071934 0.311 IGFBP.419.09024 0.821429 28 0.604167 48 76 0.072878 0.299 sIL.1RI 61.502530.704545 44 0.4375 16 60 0.073962 0.299 MIF 192.023 0.875 16 0.627119 5975 0.074126 0.225 IGF.II 304.3222 0.5 18 0.745455 55 73 0.078394 0.348Angiopoietin.2 2037.563 0.8 40 0.565217 23 63 0.080545 0.237 VEGF.A383.2805 0.785714 42 0.571429 21 63 0.086763 0.279 MMP.7 8619.5940.595238 42 0.787879 33 75 0.087197 0.313 CYFRA.21.1 1129.805 0.78125 320.538462 26 58 0.09016 0.356 C.Peptide 3468.038 0.640625 64 0.909091 1175 0.093476 0.27 CEA 24090.46 0.725806 62 0.461538 13 75 0.099668 0.212sIL.1RII 7636.054 0.6875 48 0.416667 12 60 0.102237 0.289 sVEGFR1112.065 0.416667 12 0.6875 48 60 0.102237 0.421 HGF 594.8866 0.74026 770.5 14 91 0.108812 0.452 OPN 41149.3 0.738095 42 0.5 16 58 0.118998 0.34FP 1001.211 0.738095 42 0.5 16 58 0.118998 0.451 IGFBP.7 88.263160.725806 62 0.5 14 76 0.119713 0.423 MMP.2 29712.45 0.555556 27 0.75 4875 0.121477 0.46 Beta.HCG 0.227662 0.866667 15 0.633333 60 75 0.1226520.372 EGF.1 214.3822 0.590909 22 0.806452 31 53 0.123603 0.319 HB.EGF566.2644 0.746835 79 0.5 10 89 0.135974 0.491 CA125 27.12589 0.74359 390.526316 19 58 0.137596 0.297 EGF 7.196994 0.578947 19 0.772727 44 630.138297 0.407 Leptin.1 24785.75 0.625 48 0.9 10 58 0.142086 0.412sgp130 247102.2 0.68 50 0.4 10 60 0.149211 0.483 SCF 44.52682 0.83333318 0.631579 57 75 0.15047 0.568 BMP.9 175.4831 0.641026 39 0.833333 2463 0.151565 0.529 Leptin 25030.11 0.666667 51 0.916667 12 63 0.1532030.471 MMP.9 60960.72 0.9 10 0.646154 65 75 0.153985 0.527 MMP.1 1716.1110.454545 11 0.71875 64 75 0.158025 0.535 TNF.Alpha 60.611 0.71875 640.454545 11 75 0.158025 0.471 G.CSF 29.014 0.76 50 0.538462 13 630.167521 0.572 TRAIL 43.58466 0.809524 21 0.62963 54 75 0.173113 0.63IGFBP.2 4.596466 0.5 12 0.71875 64 76 0.178147 0.6 sIL.4R 2621.5520.724138 29 0.548387 31 60 0.188122 0.579 IGFBP.6 93.69005 0.576923 260.74 50 76 0.194708 0.64 PDGF.AB.BB 53802.61 0.756757 37 0.605263 38 750.2169 0.759 Epiregulin 27.317 0.793103 29 0.625 24 53 0.226799 0.655sVEGFR2 15112.92 0.682927 41 0.526316 19 60 0.263893 0.675 FGF.122.43221 0.74359 78 0.545455 11 89 0.27982 0.558 HE4 2394.042 0.73809542 0.606061 33 75 0.318789 0.847 IGFBP.1 4.214632 0.742857 35 0.63414641 76 0.33443 0.836 TGF.alpha 25.20851 0.655172 58 0.777778 18 760.396001 0.889 IL.8 8.220301 0.6 20 0.714286 56 76 0.405188 0.812 FGF.243.838 0.735849 53 0.6 10 63 0.452219 0.778 PLGF 19.93105 0.75 560.666667 33 89 0.466906 0.959

TABLE IV Post Treatment Cohort Prop. ≤ No. ≤ Prop. > No > Total OptimalAdjusted Biomarker Cutoff cutoff Cutoff Cutoff Cutoff No. p-valuep-value TRAIL 60.26394 0.485714 35 0.828947 76 111 0.00046 0.003 sTNFRI1506.637 0.83871 62 0.5 34 96 0.000738 0.005 IGFBP.1 9.170897 0.79545588 0.409091 22 110 0.000918 0.005 sEGFR 50565.21 0.5 36 0.823529 68 1040.001168 0.007 IGF.1 7807.722 0.5 32 0.807692 78 110 0.002077 0.008TGF.beta1 9073.747 0.3 10 0.792208 77 87 0.002814 0.016 HGF 239.720.802469 81 0.5 30 111 0.003543 0.034 MMP.7 8326.919 0.823529 68 0.5581443 111 0.004292 0.024 MMP.2 33782.48 0.516129 31 0.8 80 111 0.0044350.027 FP 964.1461 0.650794 63 0.9 40 103 0.005027 0.031 OPN 34067.170.877551 49 0.62963 54 103 0.005844 0.034 sVEGFR2 10846.5 0.384615 130.771084 83 96 0.007303 0.049 IL.6 8.036509 0.833333 66 0.594595 37 1030.009841 0.035 CYFRA.21.1 768.7162 0.862745 51 0.634615 52 103 0.011850.03 IGF.II 507.7223 0.540541 37 0.794118 68 105 0.01287 0.083 sTNFRII7588.941 0.8 65 0.548387 31 96 0.015052 0.093 CA.15.3 34.52987 0.78313383 0.535714 28 111 0.015644 0.056 sCD30 129.7465 0.657534 73 0.913043 2396 0.017669 0.081 sIL.4R 2096.678 0.428571 14 0.768293 82 96 0.0201190.103 sIL.2Ralpha 836.577 0.818182 55 0.585366 41 96 0.020777 0.118IGFBP.7 53.66313 0.944444 18 0.673913 92 110 0.021058 0.137 TNF.Alpha91.0655 0.666667 93 0.941176 17 110 0.021294 0.114 VEGF 1297.73 0.75510298 0.428571 14 112 0.022711 0.05 Follistatin 968.871 0.363636 11 0.9 1021 0.023736 0.013 Leptin.1 11840.03 0.654545 55 0.854167 48 103 0.0238870.154 IGFBP.3 1486.814 0.766667 90 0.5 20 110 0.026339 0.161 PLGF86.76473 0.710145 69 0.3 10 79 0.026822 0.127 IGFBP.5 193.509 0.84090944 0.636364 66 110 0.029601 0.166 GRO 4254.61 0.65 80 0.866667 30 1100.033305 0.162 IL.8 63.90276 0.75 96 0.466667 15 111 0.033355 0.178 HE42993.413 0.772727 88 0.521739 23 111 0.034259 0.201 IGFBP.2 22.896570.796875 64 0.608696 46 110 0.034418 0.169 PDGF.AA 10851.41 0.416667 120.744898 98 110 0.03748 0.218 Beta.HCG 0.1585 0.884615 26 0.670588 85111 0.044598 0.239 RANTES 112016.4 0.677083 96 0.928571 14 110 0.0624670.353 Amphiregulin 18.45182 0.818182 22 0.588235 51 73 0.066034 0.236FGF.2.1 66.75736 0.677966 59 0.840909 44 103 0.069766 0.338 sgp130242843.7 0.771429 70 0.576923 26 96 0.075576 0.348 C.Peptide 3348.4110.734694 98 0.454545 11 109 0.078178 0.289 CA.19.9 9.618456 0.615385 390.777778 72 111 0.079573 0.34 Endoglin 662.959 0.4 10 0.818182 11 210.080495 0.042 Total.PSA 528.9822 0.764706 85 0.576923 26 111 0.0808080.25 Tenascin.C 3835.28 0.698413 63 0.4 10 73 0.081497 0.309 PDGF.AB.BB66331.14 0.673913 92 0.888889 18 110 0.089174 0.436 sVEGFR1 360.34840.763158 76 0.55 20 96 0.091301 0.412 TGF.alpha 9.548921 0.783333 600.634615 52 112 0.096262 0.408 HB.EGF 311.0071 0.542857 35 0.733333 4580 0.099623 0.442 sVEGFR3 2158.519 0.66129 62 0.823529 34 96 0.1028680.428 IGFBP.4 79.29117 0.752809 89 0.571429 21 110 0.111044 0.546 MMP.16532.742 0.766234 77 0.617647 34 111 0.115508 0.467 sIL.1RI 65.470950.753086 81 0.533333 15 96 0.116428 0.506 CA125 56.64133 0.777778 810.636364 22 103 0.180181 0.453 MIF 303.2853 0.671875 64 0.787234 47 1110.204763 0.685 MMP.9 55018.82 0.833333 24 0.689655 87 111 0.204937 0.752FGF.1 10.48625 0.6 50 0.758621 29 79 0.218824 0.596 SCF 77.97 0.74193593 0.611111 18 111 0.264052 0.827 sIL.1RII 9586.476 0.697674 86 0.9 1096 0.273533 0.817 PDGF.BB 28385.66 0.68254 63 0.5 10 73 0.295399 0.791Betacellulin 17.683 0.717949 39 0.588235 34 73 0.323903 0.877 Prolactin15306.05 0.764045 89 0.642857 14 103 0.335635 0.848 EGF.1 31.146020.785714 14 0.627119 59 73 0.354469 0.865 sIL.6R 16360.28 0.683333 600.777778 36 96 0.357432 0.938 MMP.10 290.6754 0.75 72 0.666667 39 1110.380985 0.877 G.CSF 4.271 0.727273 11 0.5 10 21 0.386997 0.252 BMP.9167.918 0.5 10 0.727273 11 21 0.386997 0.228 Leptin 10637.02 0.5 100.727273 11 21 0.386997 0.236 VEGF.C 75.486 0.5 10 0.727273 11 210.386997 0.225 VEGF.A 228.021 0.727273 11 0.5 10 21 0.386997 0.232 CEA24257.18 0.702128 94 0.823529 17 111 0.388648 0.739 TNF.alpha 2.9759290.666667 21 0.768293 82 103 0.400402 0.966 SRAGE 54.18307 0.8 200.697368 76 96 0.417848 0.922 Epiregulin 71.12777 0.678571 56 0.58823517 73 0.564203 0.636 IGFBP.6 150.9202 0.704545 88 0.772727 22 1100.605457 1.00 EGF 5.662 0.7 10 0.545455 11 21 0.659443 0.542Angiopoietin.2 1761.646 0.545455 11 0.7 10 21 0.659443 0.524Endothelin.1 3.598 0.7 10 0.545455 11 21 0.659443 0.392 VEGF.D 49.6260.545455 11 0.7 10 21 0.659443 0.571

The practice of the present invention will employ, unless otherwiseindicated, conventional methods for measuring the level of the biomarkerwithin the skill of the art. The techniques may include, but are notlimited to, molecular biology and immunology. Such techniques areexplained fully in the literature. See, e.g., Sambrook, et al. MolecularCloning: A Laboratory Manual (Current Edition, Cold Spring HarborLaboratory Press, Cold Spring Harbor, N.Y.); Current Protocols inMolecular Biology (Eds. A Ausubel et al., NY: John Wiley & Sons, CurrentEdition); DNA Cloning: A Practical Approach, vol. I & II (D. Glover,ed.); Oligonucleotide Synthesis (N. Gait, ed., Current Edition); NucleicAcid Hybridization (B. Hames & S. Higgins, eds., Current Edition);Transcription and Translation (B. Hames & S. Higgins, eds., CurrentEdition).

The above Figures and disclosure are intended to be illustrative and notexhaustive. This description will suggest many variations andalternatives to one of ordinary skill in the art. All such variationsand alternatives are intended to be encompassed within the scope of theattached claims. Those familiar with the art may recognize otherequivalents to the specific embodiments described herein whichequivalents are also intended to be encompassed by the attached claims.

REFERENCES

-   1. ACS. American Cancer Society. Cancer Facts & Figures. 2013. 2013    [cited 2013 Jun. 6, 2013]; Available from:    http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-036845.pdf-   2. Jemal A, Center M M, Ward E, Thun M J. Cancer occurrence. Methods    Mol Biol 2009; 471: 3-29.-   3. Jemal A, Thun M J, Ries L A, et al. Annual report to the nation    on the status of cancer, 1975-2005, featuring trends in lung cancer,    tobacco use, and tobacco control. J Natl Cancer Inst 2008; 100:    1672-94.-   4. Fidias P M, Dakhil S R, Lyss A P, et al. Phase III study of    immediate compared with delayed docetaxel after front-line therapy    with gemcitabine plus carboplatin in advanced non-small-cell lung    cancer. J Clin Oncol 2009; 27: 591-8.-   5. Goldstraw P, Crowley J, Chansky K, et al. The IASLC Lung Cancer    Staging Project: proposals for the revision of the TNM stage    groupings in the forthcoming (seventh) edition of the TNM    Classification of malignant tumours. J Thorac Oncol 2007; 2: 706-14.-   6. Groome P A, Bolejack V, Crowley J J, et al. The IASLC Lung Cancer    Staging Project: validation of the proposals for revision of the T,    N, and M descriptors and consequent stage groupings in the    forthcoming (seventh) edition of the TNM classification of malignant    tumours. J Thorac Oncol 2007; 2: 694-705.-   7. Borgia J A, Basu S, Faber L P, et al. Establishment of a    multi-analyte serum biomarker panel to identify lymph node    metastases in non-small cell lung cancer. J Thorac Oncol 2009; 4:    338-47.-   8. Farlow E C, Patel K, Basu S, et al. Development of a multiplexed    tumor-associated autoantibody-based blood test for the detection of    non-small cell lung cancer. Clin Cancer Res 2010; 16: 3452-62.-   9. Patel K, Farlow E C, Kim A W, et al. Enhancement of a    multianalyte serum biomarker panel to identify lymph node metastases    in non-small cell lung cancer with circulating autoantibody    biomarkers. Int J Cancer 2010; 129: 133-42.-   10. Shersher D D, Vercillo M S, Fhied C, et al. Biomarkers of the    Insulin-Like Growth Factor Pathway Predict Progression and Outcome    in Lung Cancer. Ann Thorac Surg 2011.-   11. Borgia J A, Basu S, Faber L P, et al. Establishment of a    multi-analyte serum biomarker panel to identify lymph node    metastases in non-small cell lung cancer. J Thorac Oncol 2009; 4:    338-47.-   12. R Core Team (2013). R: A language and environment for    statistical computing. R Foundation for Statistical Computing,    Vienna, Austria URL http://www.R-proiect.org/

1. A method for identifying rapidly progressing lung cancer in asubject, the method comprising: obtaining a biological sample from thesubject; assaying a level of a biomarker in a biomarker panel in thebiological sample, the panel comprising at least one biomarker selectedfrom Table I or Table II; determining whether the subject is treatmentnaïve or has received at least one treatment; comparing the level of thebiomarker in the subject's sample to a cutoff value listed in Table Ifor treatment naïve subjects or Table II for previously treatedsubjects; determining whether the subject's level is above or below thecutoff value to determine whether the subject has rapidly progressinglung cancer.
 2. The method according to claim 1, wherein the at leastone biomarker assayed has a p-value p≤0.05.
 3. The method according toclaim 1, wherein the at least one biomarker assayed has a p-value wherep≤0.01.
 4. The method according to claim 1, comprising determining thelevel of at least two biomarkers selected from Table I for treatmentnaïve subjects or from Table II for previously treated subjects.
 5. Themethod according to claim 1, comprising determining the level of thebiomarker for the panel of biomarkers wherein the at least one biomarkerin the panel is selected from sTNFRI, sTNFRII, CA 19-9, Follistatin,Total PSA, TNF-α and IL-6 for treatment naïve subjects.
 6. The methodaccording to claim 1, comprising determining the level of the biomarkerfor the panel of biomarkers wherein the at least one biomarker in thepanel comprises TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1, TGF-β, HGF, MMP-7,MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6 for the pretreatedsubjects.
 7. The method according to claim 1, wherein the lung cancer isnon-small cell lung cancer.
 8. The method according to claim 1, whereinthe biological sample comprises plasma sample or serum sample.
 9. Themethod according to claim 1, further comprising modifying a treatmentregime for the subject when the comparison indicates that the subjecthas rapidly progressing lung cancer.
 10. A kit for performing themeasurement of the level of the biomarker of the subject in claim 1,wherein the kit comprises reagents for measuring the at least onebiomarker.
 11. The kit according to claim 10, wherein the kit comprisesreagents for measuring serum or plasma.
 12. The kit according to claim10, wherein the kit comprises reagents for measuring the at least onebiomarker in the panel wherein the at least one biomarker has a p-valuewhere p≤0.05.
 13. The kit according to claim 10, wherein the kitcomprises reagents for measuring the at least one biomarker in the panelwherein the at least one biomarker is selected from sTNFRI, sTNFRII, CA19-9, Follistatin, Total PSA, TNF-α and IL-6 for treatment naïvesubjects.
 14. The kit according to claim 10, wherein the kit comprisesreagents for measuring the at least one biomarker, wherein the at leastone biomarker is selected from TRAIL, sTNFRI, IGFBP-1, sEGFR, IGF-1,TGF-β, HGF, MMP-7, MMP-2, α-fetoprotein, Osteopontin, sVEGFR2 and IL-6for the pretreated subjects.